|SCIENTIFIC DISCIPLINARY SECTOR||MAT/08|
These lectures provide the students with the comprehension of the main conceptual and computational tools concerned with the interpretation of big amount of data with predictive purposes. Specifically, as far as the data analysis is concerned, the lectures will describe the crucial aspects related to the processing of time series, introduce the Bayesian analysis and filtering, and provide the basics of pattern recognition. The second part of the lectures will be devoted to discuss the main predictive approaches, including regularization theory, machine and deep learning. The teaching approach will combine theoretical aspectes with focus on applications in economics and other applied sciences
The aim of these lectures is to provide students with a fair understanding of the main conceptual and computational tools concerned with the interpretation of big amount of data and with the use of such data for predictive purposes.
The first part of the course involves the crucial aspected related to the processing of time series, the Bayesian analysis and filtering and the basics of pattern recognition. The second part will discuss the main predictive approaches, such as regularization theory, machine and deep learning. The teaching approach will combine the description of the main theoretical aspects of data analysis with some focus on applications in economics and applied sciences
At the end of the course the students will gain some insights in the computational data analysis world and in the comprehension of some aspects of artificial intelligence. Further, they will obtain skills concerning the computational tools for the processing of data and time series with predictive purposes
R or Matlab programming
Data formats and I/O issues
Basic aspects of numerical analysis and statistics
Frontal teaching and computer projects
Prediction from data:
Office hours: Office hours by appointment via email
FEDERICO BENVENUTO (President)
MICHELE PIANA (President)
To be decided
All class schedules are posted on the EasyAcademy portal.